Date of Defense


Date of Graduation




First Advisor

Paul Solomon

Second Advisor

Loren Heun


What was once only depicted in science fiction is now a reality: computers are taking jobs from humans. As technology improves, automation is transforming the workplace. They say a “fourth industrial revolution” is inevitable within the next ten years. In the industrial revolution, the jobs lost were unskilled laborers, such as coal miners, textiles manufacturers, or cotton workers. There was no argument for whether or not a machine could do the jobs more efficiently--it was fact. The term technological unemployment means the loss of jobs caused by technological change. The headline, “Factory workers replaced by automation,” is not particularly startling to anyone. What may be surprising is what they say may happen in next next few decades: the replacement of highly skilled jobs with computers and robots.

Just this year, a law firm hired the first ever artificially intelligent attorney (De Jesus). Jill Watson is an A.I. teaching assistant who successfully assisted an entire semester at Georgia Technological University (Hill). A.I.’s have the capacity and potential to do more than we have ever dreamed. AI’s are “taught” through a process called machine learning, which is a way of teaching a computer to learn from information which it already has.

There are some who think it is possible for data scientists to be among those who lose their jobs to computers and machines. The purpose of this paper is to present counter-arguments to this idea. Data science is a science driven by creativity, and we believe that there are human components to it which a computer cannot do. There are limitations of the computerization of machine learning and data analysis. This paper will delve into some of these limitations, and paint a picture of the evolution of the career. Creativity, intuition, and interpretation are vital skills of data scientists and driving forces behind why artificially intelligent beings will not take these jobs.

Access Setting

Honors Thesis-Open Access